US 2025/0094504 A1Published application

Generating Enhanced Queries Using Machine Learning Models

AssigneeOracle International Corporation

InventorsVenkatakrishnan Gopalakrishnan, May Bich Nhi Lam, Diego Ceferino Torres Dho, Jan Sterba

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Abstract

Techniques for generating terms to replace an initial set of search terms for a query are disclosed. A system generates a training data set for training a machine learning model. Generating the training data set includes generating search value vectors for each of a set of labels based on sets of search values associated respectively with the labels in the set of labels. The system trains a machine learning model to predict a target label for a target search vector based on the set of labels and the respectively associated search value vectors. The system generates a target search value vector based on an initial set of search values. The system then applies the trained machine learning model to the target search value vector to predict the target label. The target label is used as a search term, that replaces the initial set of search values, for executing the query.

Claims24

Dependent claims

A non-transitory computer readable medium comprising instructions which, when executed by one or more hardware processors, causes performance of operations comprising:

obtaining (a) a plurality of labels and (b) a plurality of sets of search values, each of the plurality of labels being associated with a respective set of search values from the plurality of sets of search values;
aggregating the search values in the plurality of the sets of search values to generate a list of n search values;
generating a plurality of search value vectors for the plurality of labels, respectively, at least by:
for each particular label of the plurality of labels, generating a particular search value vector representing information for one or more particular search values in a particular set of search values associated with the particular label, wherein the information for the one or more particular search values comprises one or more of:
(a) locations of the one or more particular search values within the list of n search values,
(b) a term frequency (tf) of the one or more particular search values in the particular set of search values, or
(c) an inverse document frequency (idf) of the one or more particular search values in the list of n search values;
generating training data, for training a machine learning model, based on the plurality of labels and the plurality of search value vectors;
training the machine learning model to predict labels from vectors using the training data;
receiving a query comprising a target set of one or more target search values;
computing a target search value vector representing information for the target set of one or more target search values, wherein the information for the one or more target search values comprises one or more of:
(a) locations of the one or more target search values within the list of n search values,
(b) a term frequency (tf) of the one or more target search values in the target set of search values, or
(c) an inverse document frequency (idf) of the one or more target search values in the list of n search values;
applying the machine learning model to the target search value vector to predict a target label for the query;
and
using the target label, as a search term, to execute the query.

14 more elements

A method comprising:

obtaining (a) a plurality of labels and (b) a plurality of sets of search values, each of the plurality of labels being associated with a respective set of search values from the plurality of sets of search values;
aggregating the search values in the plurality of the sets of search values to generate a list of n search values;
generating a plurality of search value vectors for the plurality of labels, respectively, at least by:
for each particular label of the plurality of labels, generating a particular search value vector representing information for one or more particular search values in a particular set of search values associated with the particular label, wherein the information for the one or more particular search values comprises one or more of:
(a) locations of the one or more particular search values within the list of n search values,
(b) a term frequency (tf) of the one or more particular search values in the particular set of search values, or
(c) an inverse document frequency (idf) of the one or more particular search values in the list of n search values;
generating training data, for training a machine learning model, based on the plurality of labels and the plurality of search value vectors;
training the machine learning model to predict labels from vectors using the training data;
receiving a query comprising a target set of one or more target search values;
computing a target search value vector representing information for the target set of one or more target search values, wherein the information for the one or more target search values comprises one or more of:
(a) locations of the one or more target search values within the list of n search values,
(b) a term frequency (tf) of the one or more target search values in the target set of search values, or
(c) an inverse document frequency (idf) of the one or more target search values in the list of n search values;
applying the machine learning model to the target search value vector to predict a target label for the query;
and
using the target label, as a search term, to execute the query.

14 more elements

A system comprising:

one or more processors;
and
memory storing instructions that, when executed by the one or more processors, cause the system to perform:
obtaining (a) a plurality of labels and (b) a plurality of sets of search values, each of the plurality of labels being associated with a respective set of search values from the plurality of sets of search values;
aggregating the search values in the plurality of the sets of search values to generate a list of n search values;
generating a plurality of search value vectors for the plurality of labels, respectively, at least by:
for each particular label of the plurality of labels, generating a particular search value vector representing information for one or more particular search values in a particular set of search values associated with the particular label, wherein the information for the one or more particular search values comprises one or more of:
(a) locations of the one or more particular search values within the list of n search values,
(b) a term frequency (tf) of the one or more particular search values in the particular set of search values, or
(c) an inverse document frequency (idf) of the one or more particular search values in the list of n search values;
generating training data, for training a machine learning model, based on the plurality of labels and the plurality of search value vectors;
training the machine learning model to predict labels from vectors using the training data;
receiving a query comprising a target set of one or more target search values;
computing a target search value vector representing information for the target set of one or more target search values, wherein the information for the one or more target search values comprises one or more of:
(a) locations of the one or more target search values within the list of n search values,
(b) a term frequency (tf) of the one or more target search values in the target set of search values, or
(c) an inverse document frequency (idf) of the one or more target search values in the list of n search values;
applying the machine learning model to the target search value vector to predict a target label for the query;
and
using the target label, as a search term, to execute the query.

17 more elements

Dependencies are inferred from the claim text.